To detect multicollinearity, I begin by analyzing the correlation matrix, which calculates the pairwise correlation between numerical features. High correlation coefficients—values close to +1 or -1—indicate strong linear relationships. These relationships can be easily visualized using heatmaps, which help highlight highly correlated feature pairs. Scatter plots are also useful for visualizing the relationship between feature pairs, revealing both linear and non-linear correlations. Another important method is the Variance Inflation Factor (VIF), which quantifies how much the variance of an estimated regression coefficient is increased due to multicollinearity. As a rule of thumb, VIF values greater than 5 or 10 are indicative of significant multicollinearity.

To address multicollinearity, I may remove redundant features when two or more are highly correlated. The decision on which feature to remove depends on domain expertise and the specific objectives of the analysis. Alternatively, correlated features can be combined into a single, more informative variable using techniques such as averaging or creating interaction terms. Principal Component Analysis (PCA) is another effective approach; it transforms the original correlated variables into a new set of uncorrelated principal components, thereby reducing dimensionality. Additionally, regularization techniques like Ridge and Lasso regression, typically applied during the modeling phase, can help reduce the effects of multicollinearity by penalizing large coefficient estimates.